在TF 1.x中，可以使用自定义变量构建图层。这是一个例子：
import numpy as np
import tensorflow as tf
def make_custom_getter(custom_variables):
def custom_getter(getter, name, **kwargs):
if name in custom_variables:
variable = custom_variables[name]
else:
variable = getter(name, **kwargs)
return variable
return custom_getter
# Make a custom getter for the dense layer variables.
# Note: custom variables can result from arbitrary computation;
# for the sake of this example, we make them just constant tensors.
custom_variables = {
"model/dense/kernel": tf.constant(
np.random.rand(784, 64), name="custom_kernel", dtype=tf.float32),
"model/dense/bias": tf.constant(
np.random.rand(64), name="custom_bias", dtype=tf.float32),
}
custom_getter = make_custom_getter(custom_variables)
# Compute hiddens using a dense layer with custom variables.
x = tf.random.normal(shape=(1, 784), name="inputs")
with tf.variable_scope("model", custom_getter=custom_getter):
Layer = tf.layers.Dense(64)
hiddens = Layer(x)
print(Layer.variables)
构造的密集层的打印变量将是我们在custom_variables字典中指定的自定义张量：
[<tf.Tensor 'custom_kernel:0' shape=(784, 64) dtype=float32>, <tf.Tensor 'custom_bias:0' shape=(64,) dtype=float32>]
这样一来，我们就可以创建custom_variables直接使用提供的张量作为权重的图层/模型，以便我们可以进一步根据custom_variables可能依赖的任何张量来区分图层/模型的输出（特别是对于实现在调制子模块中的功能很有用网络，参数生成，元学习等）。
变量作用域用于简化使用自定义getter将所有图构建嵌套在作用域内的情况，并在提供的张量之上作为其参数构建模型。由于在TF 2.0中不再建议使用会话和可变作用域（并且所有这些低级内容已移至tf.compat.v1），使用Keras和TF 2.0实施上述方法的最佳实践是什么？
（有关GitHub的问题。）
以下是适用于TF2中任意Keras模型的通用解决方案。
首先，我们需要使用以下签名定义一个辅助功能canonical_variable_name和一个上下文管理器custom_make_variable（请参阅meta-blocks库中的实现）。
def canonical_variable_name(variable_name: str, outer_scope: str):
"""Returns the canonical variable name: `outer_scope/.../name`."""
# ...
@contextlib.contextmanager
def custom_make_variable(
canonical_custom_variables: Dict[str, tf.Tensor], outer_scope: str
):
"""A context manager that overrides `make_variable` with a custom function.
When building layers, Keras uses `make_variable` function to create weights
(kernels and biases for each layer). This function wraps `make_variable` with
a closure that infers the canonical name of the variable being created (of the
form `outer_scope/.../var_name`) and looks it up in the `custom_variables` dict
that maps canonical names to tensors. The function adheres the following logic:
* If there is a match, it does a few checks (shape, dtype, etc.) and returns
the found tensor instead of creating a new variable.
* If there is a match but checks fail, it throws an exception.
* If there are no matching `custom_variables`, it calls the original
`make_variable` utility function and returns a newly created variable.
"""
# ...
使用这些函数，我们可以使用自定义张量作为变量来创建任意Keras模型：
import numpy as np
import tensorflow as tf
canonical_custom_variables = {
"model/dense/kernel": tf.constant(
np.random.rand(784, 64), name="custom_kernel", dtype=tf.float32),
"model/dense/bias": tf.constant(
np.random.rand(64), name="custom_bias", dtype=tf.float32),
}
# Compute hiddens using a dense layer with custom variables.
x = tf.random.normal(shape=(1, 784), name="inputs")
with custom_make_variable(canonical_custom_variables, outer_scope="model"):
Layer = tf.layers.Dense(64)
hiddens = Layer(x)
print(Layer.variables)
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